Postoperative infections remain a serious threat to transplant recipients, with incidence rates as high as 30%–80% within the first month. While previous models have focused on clinical and immunological factors, a new study introduces a more comprehensive approach by incorporating lifestyle and psychological variables.
Led by Professor Ning-Ning Liu from Shanghai Jiao Tong University School of Medicine, the research team conducted a prospective observational study across six major transplant centers in China. They collected standardized data from 615 liver and kidney transplant patients, including dietary habits, psychological status, and clinical indicators.
Using machine learning and multivariate regression, the team identified several novel risk factors. For example, regular tea consumption was associated with a 57% lower risk of infection, while higher guilt scores on the Transplant Effects Questionnaire (TxEQ) were linked to a 12-fold increase in infection risk. Preoperative serum creatinine levels also played a significant role.
The resulting prediction model—validated using ROC curves, precision-recall analysis, and decision-curve analysis—showed robust performance in stratifying patients into high- and low-risk groups. Although performance dipped in the validation set, the model still provided clinically useful thresholds for intervention.
The authors suggest that future studies include more transplant types and pathogen-specific data to refine the model further.